1. Field of Invention
The field of the currently claimed embodiments of this invention relates to seizure detection devices and systems.
2. Discussion of Related Art
Epilepsy has a prevalence of about 1% in children and adults 1-8, and is characterized by chronically recurring seizures without clear precipitants 12. A seizure is a finite-time episode of disturbed cerebral function with abnormal, excessive, and synchronous electrical discharges in large groups of cortical neurons 9. Disturbances may be associated with debilitating phenomena (e.g., convulsions, low responsiveness, etc.) or remain clinically unapparent, have a duration ranging from seconds to minutes 33, and may be followed by post-ictal periods of confusion, psychosis, or sensory impairment which can last up to several hours 14-16. Epilepsy in children is associated with problems including academic achievement, behavioral and emotional adjustment, and social competence 3-5, and contributes to 0.5% of the global economic burden of diseases 12.
Despite a large variety of medications available to treat epilepsy [9]-[13], 25% of children (30% of adults) are drug-resistant. Furthermore, since medications are administered without any knowledge of an impending seizure, overtreatment is frequent and may lead to increased morbidity, psychosocial handicaps and mortality [1] [15][16]. Children are the most at risk for developing long-term morbidity, as poorly controlled seizures can affect long term cognitive development and function. Alternative treatments for drug-resistant patients include surgical resection of the epileptogenic zone [17][24] and neurostimulation [14]-[28]. Surgical resection is widely accepted but is not always possible and its success mostly depends on the correct localization of the epileptic focus [17] and the specific cortical area to be resected [24]. Chronic open- and closed-loop neurostimulation are still under clinical trials for adults, and although the results are encouraging [47][25], their therapeutic effectiveness critically depends on electrode placement, coverage, morphology of seizure, and most importantly seizure onset detection [14].
The accurate detection of seizure onsets from sequential iEEG (intracranial electroencephalography) measurements is fundamental for the development of both responsive neurostimulation and effective patient-warning devices. Several OSD (online seizure detection) algorithms have been proposed thus far [47]-[80] and though they are highly sensitive (large number of true positives), these algorithms generally have low specificity (large number of false positives), which limits their clinical use. NeuroPace Inc. has pioneered the development and testing of a closed-loop device, the RNS™ system, which automatically detects an approaching seizure by monitoring two iEEG channels and responds with high frequency periodic stimulation in drug-resistant epilepsy patients [30]. Despite promising results in small populations of patients after short-term follow ups (less than 2 years) [29][30], a recent long-term (5 years) study [31][32] has indicated that the device reduces the number of seizures by 50% in less than 30% of the patients (reduction computed vs. the baseline pre-treatment condition), which is about as effective as a new medication in patients with drug-resistant partial seizures. Although the detection algorithms can be tuned for seizures in a given patient, these simple algorithms lack specificity with many detections of inter-ictal activity that are not destined to evolve into electrical or clinical seizure activity. Since detections result in activation of closed-loop therapy, stimulations are frequently delivered when no seizure occurs. While no significant side effects of stimulation were observed in the RNS trial, increased stimulation frequency can dramatically reduce battery life (typically to 1-2 years [29]). In other studies, there are reports of possible consequences of repetitive stimulation including depression, memory impairment and confusion [49].
The lack of specificity of current OSD algorithms including the one implemented in the RNS™ system presumably occurs because (i) they compute statistics from 1-2 channels at a time that may not capture network dynamics of the brain, and/or (ii) the detection thresholds are not optimized to maximize OSD performance. By optimally detecting when a seizure occurs, specificity of detection would increase and non-specific closed-loop therapy would decrease.
Automatic online seizure detection (OSD) in intractable epilepsy has generated great interest in the last twenty years and is a fundamental step toward the development of anti-epileptic responsive neurostimulation [14]-[28]. Pioneering works in the late 1970s and 1980s by Gotman et al. [50][51] showed that seizures can be automatically separated from inter-ictal activity, and since then, several approaches to OSD have been proposed by exploiting either scalp or intracortical EEG recordings, single or multi-channel analysis, linear or nonlinear features.
Osorio et al. [52]-[55] used a wavelet-based decomposition of selected iEEG recordings to (i) separate the seizure-related component from the background noise, (ii) track the ratio between these components in the time-frequency domain, and (iii) detect a seizure when such a ratio crosses a fixed threshold for a sufficiently long time. Parameters of the detection method (e.g., threshold, duration of the supra-threshold condition, etc.) can be either fixed [52] or adaptive [53][54]. Fixed threshold-based approaches were also proposed in [56]-[58], where the threshold was applied to linear spectral features of the iEEG recordings.
Gotman et al. [59]-[61]proposed a probabilistic framework for seizure detection using scalp EEG [59][61] and iEEG [60] recordings. For each channel, amplitude and energy measures in multiple frequency bands are computed via wavelet decomposition and the corresponding probability distribution function is estimated. Then, the probability of a seizure is conditioned on the value of such measures and estimated via Bayes' rule. A patient-specific threshold is finally applied on this conditional probability of seizure to decide, for each channel, whether a seizure is likely, and a seizure is detected when that threshold is passed in a sufficient number of channels.
More recently, this paradigm has been implemented using sophisticated classification tools. In particular, iEEG channels have been processed individually to extract multiple univariate or bivariate features in the time, frequency domain or the wavelet [62]-[78] domain. Then, for each channel, the features have been combined into vectors and classified via support vector machines (SVMs) [67][69][72][75], principal components analysis (PCA) [73][74], artificial neural networks (ANNs) [62][64]-[66][70][76]-[78], fuzzy logics [68], or pattern recognition tools [63]. Finally, decisions made for different channels are combined or ranked to ultimately determine whether or not a seizure has occurred. As a variation to this paradigm, [72][79] merged features extracted from different channels into one vector and applied a classification rule on this vector.
In the current paradigm, OSD is solved by (i) computing a statistic from a few iEEG measurements at a time, and (ii) then constructing a threshold or classification rule that, based on this statistic, determines whether or not a seizure has occurred (FIG. 2A). The choice of the threshold is traditionally supervised and depends on the fluctuations of the statistic, the specific patient, or the electrode position, and requires long training sessions to be more accurate. Sophisticated classifiers generate unsupervised criteria that separate the feature space into dominant ictal and non-ictal regions but without penalties for specific performance goals (e.g. minimize false positives). All such thresholds trigger too many false alarms when applied to test data. Consequently, all efforts put towards improving OSD algorithms have been in either identifying better statistics with fancy signal processing and/or in implementing more sophisticated classifiers borrowed from the machine learning community. The fundamental problem with this paradigm is that detection performance is not measurable until after implementation (“algorithm defines performance”). There thus remains a need for improved seizure detection devices and systems.